Sense My FEUP - April 2016 Data

SenseMyFEUP data

Data loading

Data is filtered by travelmode (car and bus) and date (April 2016).

Intersession times

Visualizing intersession times

All intersession time Porto April16

Intersession time along the week

We recommend that you use the dev version of ggplot2 with `ggplotly()`
Install it with: `devtools::install_github('hadley/ggplot2')`

Histogram intersession time.

We recommend that you use the dev version of ggplot2 with `ggplotly()`
Install it with: `devtools::install_github('hadley/ggplot2')`
We recommend that you use the dev version of ggplot2 with `ggplotly()`
Install it with: `devtools::install_github('hadley/ggplot2')`

Number of sessions per way_id

Filtering by sessions per way_id

ECDF

ECDF by #sessions

Edges with >50 sessions.

We recommend that you use the dev version of ggplot2 with `ggplotly()`
Install it with: `devtools::install_github('hadley/ggplot2')`

Top 10% points

Map points April 2016

Showing maps

Traffic Map April Porto 2016 all

Traffic Map April 2016 all >50

Traffic day Map April 2016

Traffic night Map April 2016

Top 12 edges

Map Intersession time (<1.3h) day.

Map Intersession time (<2h)night.

–>

#Sessions by date >50

Day of the week

By day

By hour

Speeds >50

df_hotedges_april16pt %>% 
  group_by(hour =hour(time)) %>% 
  summarise(avg_speed = mean((speed*18)/5)) %>% 
  ggplot(aes(hour, avg_speed)) + 
  geom_line() +
  scale_x_continuous(breaks = seq(0,23,1))+
  ylab("Avg speed km/h")

Speed by week

Speed by day

Speed by hour

Number of session per hour

Ways_ids per half hour

prueba <- table(cut(filter(df_hotedges_april16pt, day(time) < 7)$time, breaks = "30 mins"))
plot(prueba, xlab = "date", ylab = "frequency") 

Time serie per way_id

filter(df_hotedges_april16pt, way_id == 37141967 ) %>% 
  group_by(day(time)) %>% 
  ggplot(aes(x= time, y= (speed*18)/5)) + 
  geom_line()